Eye care in the intensive care unit during the COVID-19 pandemic and beyond.
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ABSTRACT:
SUBMITTER: Soare C
PROVIDER: S-EPMC7300450 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Coronavirus disease 2019 (COVID-19) can lead to multiorgan damage and fatal outcomes. MicroRNAs (miRNAs) are detectable in blood, reflecting cell activation and tissue injury. We performed small RNA-Seq in healthy controls (N=11), non-severe (N=18) and severe (N=16) COVID-19 patients
2021-11-17 | GSE176498 | GEO
Project description:BackgroundFrequent visiting and communication with patients' families are embedded within normal ICU practice, however the COVID-19 pandemic has challenged this, and it is unclear how ICUs are managing. We aimed to investigate how NHS ICUs are approaching family communications and visiting during the COVID-19 pandemic.MethodsAn electronic snapshot survey was delivered between 16th April and 4th May 2020 and was open to NHS ICUs. Replies from 134 individual ICUs with COVID patients were included.ResultsAll reported that visiting was more restricted than normal with 29 (22%) not allowing any visitors, 71 (53%) allowing visitors at the end of a patient's life (EOL) only, and 30 (22%) allowing visitors for vulnerable patients or EOL. Nearly all (n = 130, 97%) were updating families daily, with most initiating the update (n = 120, 92%). Daily telephone calls were routinely made by the medical (n = 75, 55%) or nursing team (n = 50, 37%). Video calling was used by 63 (47%), and 39 (29%) ICUs had developed a dedicated family communication team. Resuscitation and EOL discussions were most frequently via telephone (n = 129, 96%), with 24 (18%) having used video calling, and 15 (11%) reporting discussions had occurred in person. Clinicians expressed their dissatisfaction with the situation and raised concerns about the detrimental effect on patients, families, and staff.ConclusionsCOVID-19 has resulted in significant changes across NHS ICUs in how they interact with families. Many units are adapting and moving toward distant and technology-assisted communication. Despite innovative solutions, challenges remain and there may be a role for local and national guidance.
| S-EPMC9403523 | biostudies-literature
Project description:ObjectivesTo examine the effect of motivational messages on optimism, hopelessness, and life satisfaction of intensive care nurses during the COVID-19 pandemic.Study designThis is a multicentre, randomized controlled, open-label study.Research methodology/designThe study was conducted with a total of 87 nurses working in the COVID-19 intensive care units of three hospitals in Istanbul. Motivational messages were sent via SMS to the participants in the motivational group (n = 41) for 21 days. The data were obtained using a Personal Information Form, the Life Orientation Test, Beck Hopelessness Scale and the Satisfaction with Life Scale.ResultsThe nurses' mean age in the motivational and control groups was 28.4 ± 7.6 and 26.9 ± 3.7 years, respectively. Before the messages, no statistically significant difference was found between the two groups in terms of life orientation (p = 0.059), hopelessness (p = 0.214), and satisfaction with life (p = 0.898) scores. After the messages, life orientation (p = 0.042) and life satisfaction (p = 0.040) scores were significantly higher in the motivational group compared with the control group, and the hopelessness score was significantly lower (p = 0.005).ConclusionAccording to our study, motivational messages sent to intensive care nurses during the pandemic increased their level of optimism and life satisfaction and decreased their level of hopelessness.Trial registrationNCT04751474.
| S-EPMC8559765 | biostudies-literature
Project description: Not available
| S-EPMC7334133 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic demanded intensive care units (ICUs) globally to expand to meet increasing patient numbers requiring critical care. Critical care nurses were a finite resource in this challenge to meet growing patient numbers, necessitating redeployment of nursing staff to work in ICUs.ObjectiveOur aim was to describe the extent and manner by which the increased demand for ICU care during the COVID-19 pandemic was met by ICU nursing workforce expansion in the late 2021 and early 2022 in Victoria, Australia.MethodsThis is a retrospective cohort study of Victorian ICUs who contributed nursing data to the Critical Health Information System from 1 December 2021 to 11 April 2022. Bedside nursing workforce data, in categories as defined by Safer Care Victoria's pandemic response guidelines, were analysed. The primary outcome was 'insufficient ICU skill mix'-whenever a site had more patients needing 1:1 critical care nursing care than the mean daily number of experienced critical care nursing staff.ResultsOverall, data from 24 of the 47 Victorian ICUs were eligible for analysis. Insufficient ICU skill mix occurred on 10.3% (280/2725) days at 66.7% (16/24) of ICUs, most commonly during the peak phase from December to mid-February. The insufficient ICU skill mix was more likely to occur when there were more additional ICU beds open over the 'business-as-usual' number. Counterfactual analysis suggested that had there been no redeployment of staff to the ICU, reduced nursing ratios, with inability to provide 1:1 care, would have occurred on 15.2% (415/2725) days at 91.7% (22/24) ICUs.ConclusionThe redeployment of nurses into the ICU was necessary. However, despite this, at times, some ICUs had insufficient staff to cope with the number and acuity of patients. Further research is needed to examine the impact of ICU nursing models of care on patient outcomes and on nurse outcomes.
| S-EPMC9742212 | biostudies-literature
Project description:The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.
| S-EPMC10404188 | biostudies-literature
Project description:Ocular surface disease is common in the intensive care population with 20-42% of patients developing corneal epithelial defects. The ocular surface is normally protected by the ability to produce tears, to blink and to close the eyes with rest or sleep. All of these mechanisms can be disrupted in the intensive care population, increasing the risk of developing ocular surface disease. Despite the scale of the problem, eye-care protocols are commonly not instigated and documentation of eye care is often poor. This review details the risk factors for developing ocular surface disease. It also provides evidence-based guidance on protecting the eyes in vulnerable patients, identifying diseases affecting the eye in intensive care patients and delivering the best treatment to the eye. There is growing evidence that adherence to a correctly performed eye-care guideline prevents the majority of corneal problems encountered in the intensive care unit.
| S-EPMC6259085 | biostudies-other
Project description:The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.
| S-EPMC8460747 | biostudies-literature
Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
| 2346925 | ecrin-mdr-crc